{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>30.57</td>\n",
       "      <td>30.57</td>\n",
       "      <td>28.63</td>\n",
       "      <td>28.78</td>\n",
       "      <td>70997200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>28.41</td>\n",
       "      <td>29.54</td>\n",
       "      <td>28.23</td>\n",
       "      <td>29.23</td>\n",
       "      <td>87498504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>29.03</td>\n",
       "      <td>29.39</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.26</td>\n",
       "      <td>48012112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.25</td>\n",
       "      <td>27.73</td>\n",
       "      <td>28.50</td>\n",
       "      <td>23647604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.18</td>\n",
       "      <td>27.63</td>\n",
       "      <td>28.67</td>\n",
       "      <td>98239664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close    volume\n",
       "0  2016-01-04  30.57  30.57  28.63  28.78  70997200\n",
       "1  2016-01-05  28.41  29.54  28.23  29.23  87498504\n",
       "2  2016-01-06  29.03  29.39  28.73  29.26  48012112\n",
       "3  2016-01-07  28.73  29.25  27.73  28.50  23647604\n",
       "4  2016-01-08  28.73  29.18  27.63  28.67  98239664"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = pd.read_csv('zgpa_train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "731\n"
     ]
    }
   ],
   "source": [
    "price = data.loc[:,'close']\n",
    "num_data = price.shape[0]\n",
    "print(num_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(730,) (730,)\n"
     ]
    }
   ],
   "source": [
    "X = price[0:num_data-1]\n",
    "y = price[1:num_data]\n",
    "print(X.shape,y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "fig1 = plt.figure(figsize=(7,5))\n",
    "plt.plot(y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d36a827eb8>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#normalize the data\n",
    "X_norm = X/max(X)\n",
    "y_norm = y/max(y)\n",
    "X_train = X_norm\n",
    "y_train = y_norm\n",
    "plt.plot(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(730, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.array(X_train).reshape(num_data-1,1,1)\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LSTM,SimpleRNN\n",
    "model = Sequential()\n",
    "model.add(SimpleRNN(units=5,input_shape = (730,1),activation='relu'))\n",
    "model.add(Dense(units=1,activation='linear'))\n",
    "model.compile(optimizer='adam',loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "simple_rnn_7 (SimpleRNN)     (None, 5)                 35        \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 41\n",
      "Trainable params: 41\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(730, 1, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input arrays should have the same number of samples as target arrays. Found 1 input samples and 730 target samples.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-e0fcf19ae589>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m730\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\envs\\env_ttest\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m   1152\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1153\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1154\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m   1155\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1156\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\env_ttest\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    635\u001b[0m             \u001b[1;31m# Check that all arrays have the same length.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    636\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mcheck_array_lengths\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 637\u001b[1;33m                 \u001b[0mtraining_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcheck_array_length_consistency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    638\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_graph_network\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    639\u001b[0m                 \u001b[1;31m# Additional checks to avoid users mistakenly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\env_ttest\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mcheck_array_length_consistency\u001b[1;34m(inputs, targets, weights)\u001b[0m\n\u001b[0;32m    242\u001b[0m                          \u001b[1;34m'the same number of samples as target arrays. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    243\u001b[0m                          \u001b[1;34m'Found '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' input samples '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 244\u001b[1;33m                          'and ' + str(list(set_y)[0]) + ' target samples.')\n\u001b[0m\u001b[0;32m    245\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset_w\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    246\u001b[0m         raise ValueError('All sample_weight arrays should have '\n",
      "\u001b[1;31mValueError\u001b[0m: Input arrays should have the same number of samples as target arrays. Found 1 input samples and 730 target samples."
     ]
    }
   ],
   "source": [
    "model.fit(X_train.reshape(1,730,1),y_train,batch_size=30,epochs=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make prediction\n",
    "y_train_predict = model.predict(X_train)\n",
    "y_train_predict = y_train_predict*max(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=(7,5))\n",
    "real_price = plt.plot(y,label='real_price')\n",
    "predict_price = plt.plot(y_train_predict,label='predict_price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_test = pd.read_csv('zgpa_test.csv')\n",
    "data_test.head()\n",
    "price_test = data_test.loc[:,'close']\n",
    "num_test = price_test.shape[0]\n",
    "X_test = price_test[0:num_test-1]\n",
    "y_test = price_test[1:num_test]\n",
    "X_test = X_test/max(X)\n",
    "X_test = np.array(X_test).reshape(num_test-1,1,1)\n",
    "y_predict_test = model.predict(X_test)\n",
    "y_predict_test = y_predict_test*max(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = plt.figure(figsize=[7,5])\n",
    "plt.plot(y_test,label='real price(test)')\n",
    "plt.plot(y_predict_test,label='predict price(test)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181, 1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.concatenate((np.array(y_test).reshape(-1,1),y_predict_test),axis = 1)\n",
    "result = pd.DataFrame(result,columns=['real_price','predicted_price'])\n",
    "result.head()\n",
    "result.to_csv('zgpa_predict2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
